Vehicle-to-grid bidirectional energy optimization interaction method and system for autonomous electric vehicles

Abstract

A method and system for bidirectional energy optimization interaction between vehicles and the grid for autonomous electric vehicles is disclosed. This method first constructs a system with the objective of minimizing the overall charging cost of autonomous electric vehicles, considering the owner's driving mode, autonomous driving mode, charging of the electric vehicle, and V2G reverse discharge. Then, it solves the bidirectional energy optimization interaction model to obtain the bidirectional energy optimization interaction scheme for autonomous electric vehicles. This invention not only effectively reduces users' extra travel and lowers time costs by utilizing the fact that autonomous electric vehicles can go to charging stations during idle periods to charge and discharge, while increasing the scheduling flexibility of charging and discharging loads, but also improves the overall load flexibility of charging stations and reduces the cost of purchasing electricity from the upper-level power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle-to-grid bidirectional energy interaction, and particularly relates to a vehicle-to-grid bidirectional energy optimization interaction method and system for autonomous electric vehicles. BACKGROUND

[0002] The concept of vehicle-to-grid bidirectional energy interaction originates from the increasingly close relationship between electric vehicles and power systems, especially in response to the pressure on the power grid brought about by the rapid popularization of electric vehicles. This interaction mode has gradually attracted attention. With the rapid growth of global electric vehicle ownership, the traditional power grid is facing unprecedented challenges, such as fluctuations in peak load and the complexity of power resource allocation. In order to cope with these challenges, vehicle-to-grid bidirectional energy interaction technology has emerged. It not only allows electric vehicles to obtain energy from the power grid for charging, but also enables them to feed back stored electric energy to the power grid during periods of high demand, thereby serving as a distributed energy storage device. This mechanism of bidirectional energy flow can effectively alleviate the burden on the power grid, balance power supply and demand, and reduce the impact of peak power demand on the power grid.

[0003] The progress of autonomous driving technology is also remarkable. The core of autonomous electric vehicle charging guidance technology lies in seamless communication between the vehicle system and the charging infrastructure, intelligently selecting the best charging time and location. With continuous iteration of technology, the safety and stability of autonomous driving have been significantly improved. In addition to the daily commute of vehicle users, it is also necessary to explore the effective use of the advantages of autonomous electric vehicles in the field of vehicle-to-grid bidirectional energy optimization interaction. SUMMARY

[0004] The purpose of the present application is to overcome the above-mentioned problems existing in the prior art, and to provide a vehicle-to-grid bidirectional energy optimization interaction method and system for autonomous electric vehicles.

[0005] To achieve the above purpose, the technical solution of the present application is:

[0006] In a first aspect, the present application provides a vehicle-to-grid bidirectional energy optimization interaction method for autonomous electric vehicles, comprising:

[0007] S1, a bidirectional energy optimization interaction model of a power-traffic coupled network is constructed, the bidirectional energy optimization interaction model of the power-traffic coupled network aims to minimize the comprehensive charging cost of autonomous electric vehicles, and considers the driving mode of the owner of the electric vehicle, the unmanned driving mode, and the charging and V2G reverse discharge of the electric vehicle;

[0008] S2, the bidirectional energy optimization interaction model of the power-traffic coupled network is solved to obtain a vehicle-to-grid bidirectional energy optimization interaction scheme for autonomous electric vehicles.

[0009] The objective function of the two-way energy optimization interaction model of the power-transportation coupled network includes:

[0010] min C E +C B +C T ;

[0011]

[0012] In the above formula, C E The cost of purchasing electricity from the upper-level power grid for charging stations; C B For battery wear and tear costs; C T d represents the user's time cost; d represents the number of typical days in a year; t represents any time period within a typical day. Let t be the active power output by the distribution network to the charging station at node e; C is the electricity purchase price for time period t; BV and C BE These are the daily battery loss costs consumed when the battery is used as the on-board power battery for electric vehicles and as energy storage for charging stations. For time period t, the traffic flow of electric vehicles driven by car owners heading to the charging station via path k. For time period t, the flow of electric vehicles driven by car owners using path k to the charging station to perform V2G reverse discharge; Let be the correlation coefficient between path k and road l; Let T be the travel time on road l during time period t; CH and T DI These represent the duration of a single charge and a single V2G reverse discharge, respectively; PR T Cost per unit of time.

[0013] The bidirectional energy optimization interaction model of the power-transportation coupled network considers vehicle charging and discharging guidance constraints, which include:

[0014]

[0015]

[0016] In the above formula, and For time period t, the vehicle flow of car owners driving electric vehicles and autonomous electric vehicles traveling to the charging station via path k are respectively: is the correlation coefficient between path k and the starting point or. If it is 1, then the starting point of path k is or; k is the path to the charging station for charging and discharging; or and cs are the starting point of the journey and the node where the charging station is located, respectively. and The traffic flow of owner-driven electric vehicles and autonomous electric vehicles that travel to the charging station via path k to perform V2G reverse discharge during time period t; and For time period t, the number of electric vehicles with insufficient or sufficient power at the starting point or point are respectively: and Let t be the number of electric vehicles in use and idle states at the starting point or point in time period t. and λ represents the number of charging piles at node cs used for charging and V2G reverse discharge, respectively, during time period t; R This represents the proportion of the minimum charging demand that must be met within a typical day. Let T be the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, then the node cs is the target charging station of path k. CH and T DI These represent the average duration of a single charge and the average duration of V2G reverse discharge, respectively; T U Unit of time; This represents the number of charging piles at the charging station located at node cs.

[0017] The bidirectional energy optimization interaction model of the power-transportation coupled network also considers the charging station operation constraints throughout the entire life cycle of electric vehicle batteries, including:

[0018]

[0019]

[0020] PR UB =PR B / 2(CY V B C κ V +CY E B C κ E );

[0021]

[0022] In the above formula, For time period t, the power output from the distribution network to the charging station at node e; For time period t, the actual photovoltaic power consumed by the charging station at node e; For time period t, the output or input power of the charging station at node e, when At that time, the energy storage system is in a discharging state. At this time, the energy storage system is in a charging state; For time period t, the power of V2G reverse discharge of electric vehicles in the charging station at node e; λ C Energy conversion efficiency of electric vehicle charging stations; For time period t, the charging load of electric vehicles in the charging station at node e; and For time period t, the number of charging piles at node cs used for charging and V2G reverse discharge. P is the correlation coefficient between node cs, where the charging station is located, and node e, in the distribution network. If it is 1, then the charging station at node cs is connected to node e in the distribution network. UC P is the rated charging power of a single charging station. UD The rated V2G reverse discharge power of a single charging pile; P represents the maximum photovoltaic power output at the charging station at node e during time period t. CM and P DM These are the maximum discharge power and maximum charging power of a single energy storage unit, respectively. η represents the number of energy storage systems configured within the charging station at distribution network node e; L and η H These are the lower and upper limits of the energy state of energy storage, respectively; EC E For the installed capacity of a single energy storage unit; EC O The initial charge of a single energy storage unit; PR UB Cost per unit capacity during battery charge-discharge cycles; PR B Price per battery; CY V and CY E These refer to the cycle life of the battery when used as a vehicle power battery and as an energy storage device in a charging station, respectively; B C Design capacity for a single battery; κ V and κ E The health of the battery is measured when it is used as a vehicle power battery and when it is used as an energy storage device in a charging station. and Let t represent the charging energy and discharging energy stored in the charging station at node e during time period t. During the same time period, one of the two must be 0. For the binary variable representing the operating state of energy storage, when Energy storage and discharge, Energy storage and charging; A t,e These are auxiliary variables used in the McCormick envelope method.

[0023] The bidirectional energy optimization interaction model of the power-transportation coupled network also considers power-transportation coupled network constraints, which include:

[0024]

[0025] In the above formula, and These represent the active and reactive power on line w at time t, respectively. and These represent the basic active and reactive loads connected at node e of the distribution network during time period t; For time period t, the power output from the distribution network to the charging station at node e; w represents all lines connected to node e of the distribution network; LC w The capacity of the distribution network line w; ΔU t,w The voltage drop on the distribution network line w during time period t; and These represent the resistance and reactance of the distribution network line w, respectively; U N U is the rated voltage of the distribution network busbar; t,a and U t,b U represents the bus voltages of distribution network nodes a and b during time period t, where nodes a and b are the two endpoints of distribution network line w; t,e U is the bus voltage of distribution network node e within time period t; m and U M These are the upper and lower limits of the voltage of the distribution network bus, respectively. The correlation coefficient between path k and road l; FT t,l For time period t, the total traffic flow on road l; and For time period t, the vehicle flow of car owners driving electric vehicles and autonomous electric vehicles traveling to the charging station via path k are respectively: and For time period t, the vehicle flow of owner-driven electric vehicles and autonomous electric vehicles traveling to the charging station via path k to perform V2G reverse discharge; FC l This represents the maximum traffic capacity of road l.

[0026] Secondly, this invention proposes a vehicle-to-grid bidirectional energy optimization interaction system for autonomous electric vehicles, including a model building module and a model solving module;

[0027] The model building module is used to build a two-way energy optimization interaction model of the power-transportation coupled network. The two-way energy optimization interaction model of the power-transportation coupled network aims to minimize the overall charging cost of autonomous electric vehicles, and takes into account the owner driving mode, the unmanned driving mode of electric vehicles, as well as the charging of electric vehicles and V2G reverse discharge.

[0028] The model solving module is used to solve the two-way energy optimization interaction model of the power-transportation coupled network, and obtain the vehicle-network two-way energy optimization interaction scheme for autonomous electric vehicles.

[0029] The objective function of the two-way energy optimization interaction model of the power-transportation coupled network includes:

[0030] min C E +C B +C T ;

[0031]

[0032] In the above formula, C E The cost of purchasing electricity from the upper-level power grid for charging stations; C B For battery wear and tear costs; C T d represents the user's time cost; d represents the number of typical days in a year; t represents any time period within a typical day. Let t be the active power output by the distribution network to the charging station at node e; C is the electricity purchase price for time period t; BV and C BE These are the daily battery loss costs consumed when the battery is used as the on-board power battery for electric vehicles and as energy storage for charging stations. For time period t, the traffic flow of electric vehicles driven by car owners heading to the charging station via path k. For time period t, the flow of electric vehicles driven by car owners using path k to the charging station to perform V2G reverse discharge; Let be the correlation coefficient between path k and road l; Let T be the travel time on road l during time period t; CH and T DI These represent the duration of a single charge and a single V2G reverse discharge, respectively; PR T Cost per unit of time.

[0033] The bidirectional energy optimization interaction model of the power-transportation coupled network considers vehicle charging and discharging guidance constraints, which include:

[0034]

[0035] In the above formula, and For time period t, the vehicle flow of car owners driving electric vehicles and autonomous electric vehicles traveling to the charging station via path k are respectively: is the correlation coefficient between path k and the starting point or. If it is 1, then the starting point of path k is or; k is the path to the charging station for charging and discharging; or and cs are the starting point of the journey and the node where the charging station is located, respectively. and The traffic flow of owner-driven electric vehicles and autonomous electric vehicles that travel to the charging station via path k to perform V2G reverse discharge during time period t; and For time period t, the number of electric vehicles with insufficient or sufficient power at the starting point or point are respectively: and Let t be the number of electric vehicles in use and idle states at the starting point or point in time period t. and λ represents the number of charging piles at node cs used for charging and V2G reverse discharge, respectively, during time period t; R This represents the proportion of the minimum charging demand that must be met within a typical day. Let T be the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, then the node cs is the target charging station of path k. CH and T DI These represent the average duration of a single charge and the average duration of V2G reverse discharge, respectively; T U Unit of time; This represents the number of charging piles at the charging station located at node cs.

[0036] The bidirectional energy optimization interaction model of the power-transportation coupled network also considers the charging station operation constraints throughout the entire life cycle of electric vehicle batteries, including:

[0037]

[0038] PR UB =PR B / 2(CY V B C κ V +CY E B C κ E );

[0039]

[0040]

[0041] In the above formula, For time period t, the power output from the distribution network to the charging station at node e; For time period t, the actual photovoltaic power consumed by the charging station at node e; For time period t, the output or input power of the charging station at node e, when At that time, the energy storage system is in a discharging state. At this time, the energy storage system is in a charging state; For time period t, the power of V2G reverse discharge of electric vehicles in the charging station at node e; λ C Energy conversion efficiency of electric vehicle charging stations; For time period t, the charging load of electric vehicles in the charging station at node e; and For time period t, the number of charging piles at node cs used for charging and V2G reverse discharge. P is the correlation coefficient between node cs, where the charging station is located, and node e, in the distribution network. If it is 1, then the charging station at node cs is connected to node e in the distribution network. UC P is the rated charging power of a single charging station. UD The rated V2G reverse discharge power of a single charging pile; P represents the maximum photovoltaic power output at the charging station at node e during time period t. CM and P DM These are the maximum discharge power and maximum charging power of a single energy storage unit, respectively. η represents the number of energy storage systems configured within the charging station at distribution network node e; L and η H These are the lower and upper limits of the energy state of energy storage, respectively; EC E For the installed capacity of a single energy storage unit; EC O The initial charge of a single energy storage unit; PR UB Cost per unit capacity during battery charge-discharge cycles; PR B Price per battery; CY V and CY E These refer to the cycle life of the battery when used as a vehicle power battery and as an energy storage device in a charging station, respectively; B C Design capacity for a single battery; κ V and κ E The health of the battery is measured when it is used as a vehicle power battery and when it is used as an energy storage device in a charging station. and Let t represent the charging energy and discharging energy stored in the charging station at node e during time period t. During the same time period, one of the two must be 0. For the binary variable representing the operating state of energy storage, when Energy storage and discharge, Energy storage and charging; A t,e These are auxiliary variables used in the McCormick envelope method.

[0042] The bidirectional energy optimization interaction model of the power-transportation coupled network also considers power-transportation coupled network constraints, which include:

[0043]

[0044]

[0045] In the above formula, and These represent the active and reactive power on line w at time t, respectively. and These represent the basic active and reactive loads connected at node e of the distribution network during time period t; For time period t, the power output from the distribution network to the charging station at node e; w represents all lines connected to node e of the distribution network; LC w The capacity of the distribution network line w; ΔU t,w The voltage drop on the distribution network line w during time period t; and These represent the resistance and reactance of the distribution network line w, respectively; U N U is the rated voltage of the distribution network busbar; t,a and U t,b U represents the bus voltages of distribution network nodes a and b during time period t, where nodes a and b are the two endpoints of distribution network line w; t,e U is the bus voltage of distribution network node e within time period t; m and U M These are the upper and lower limits of the voltage of the distribution network bus, respectively. The correlation coefficient between path k and road l; FT t,l For time period t, the total traffic flow on road l; and For time period t, the vehicle flow of car owners driving electric vehicles and autonomous electric vehicles traveling to the charging station via path k are respectively: and For time period t, the vehicle flow of owner-driven electric vehicles and autonomous electric vehicles traveling to the charging station via path k to perform V2G reverse discharge; FC l This represents the maximum traffic capacity of road l.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] 1. The bidirectional energy optimization interaction model of the power-traffic coupled network proposed in this invention, based on a vehicle-to-grid bidirectional energy optimization interaction method for autonomous electric vehicles, considers the driver's driving mode, the autonomous driving mode, and the charging and V2G reverse discharge of electric vehicles. On the one hand, by utilizing the autonomous driving mode of autonomous electric vehicles to go to charging stations for charging and discharging during idle periods, it can effectively reduce users' extra travel and lower time costs, while increasing the scheduling flexibility of charging and discharging loads. On the other hand, it fully utilizes the advantages of autonomous electric vehicles, allowing them to go to charging stations for charging and discharging operations on their own during users' non-use periods, thereby achieving bidirectional interaction optimization of the vehicle-to-grid network while meeting traffic constraints.

[0048] 2. The bidirectional energy optimization interaction model of the power-transportation coupled network constructed by the vehicle-to-grid bidirectional energy optimization interaction method proposed in this invention adopts a tiered utilization approach, configuring the power batteries of retired vehicles at charging stations for secondary utilization. It also considers the impact of the number of charge-discharge cycles on the lifespan of energy storage, and adopts a multi-distributed power supply configuration of photovoltaic-energy storage-autonomous vehicle V2G to improve the overall load flexibility of charging stations and reduce the cost of purchasing electricity from the upper-level power grid. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the 20-node distribution network and 13-node transportation network structure in Embodiment 1 of the present invention.

[0050] Figure 2 This is the photovoltaic power output characteristic curve in Embodiment 1 of the present invention.

[0051] Figure 3 This refers to the time-of-use electricity price in Embodiment 1 of the present invention.

[0052] Figure 4 This is a flowchart of the method described in this invention.

[0053] Figure 5 This is a schematic diagram illustrating the architecture and operation of the charging system of the present invention.

[0054] Figure 6 This is a structural diagram of the system described in this invention. Detailed Implementation

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] This invention proposes a vehicle-to-grid (V2G) bidirectional energy optimization interaction method for autonomous electric vehicles. This method utilizes the electric vehicle's power battery for V2G reverse charging, enabling flexible bidirectional interaction between the vehicle and the grid. Simultaneously, autonomous electric vehicles can autonomously travel to charging stations during off-peak hours to perform charging and discharging operations without user intervention. This saves charging time for users and improves equipment utilization during off-peak hours for charging stations, reducing queuing time and effectively lowering the system's annual comprehensive charging cost. By optimizing the configuration of battery charging and discharging facilities and rationally arranging the charging and discharging locations and times of autonomous electric vehicles (i.e., the bidirectional energy optimization interaction scheme), this method meets the requirements of power grid access and traffic constraints while reducing the hardware configuration costs and electricity purchase costs for charging service providers and the time costs for electric vehicle users due to charging operations.

[0057] Currently, the ternary lithium batteries commonly used in new energy vehicles generally have a charge-discharge cycle life of 2000-3000 cycles. Based on an electric vehicle's total lifespan of 200,000 kilometers, this only requires 600-800 cycles. Utilizing the significant redundant cycle life of electric vehicle power batteries for V2G (Vehicle-to-Grid) can effectively improve its economic efficiency. V2G requires electric vehicles to charge during off-peak hours and discharge during peak hours, a charging and discharging method that doesn't match users' daily driving habits. Electric vehicle power batteries still retain 80% health after 2000 cycles. By employing a tiered utilization approach, the power batteries from retired vehicles can be deployed to charging stations for secondary use, taking into account the impact of the number of charge-discharge cycles on the lifespan of the energy storage.

[0058] Example 1:

[0059] This embodiment uses a topological structure such as Figure 1 The 20-node distribution network and 13-node transportation network shown are constructed as objects, as follows: Figure 3 The charging system architecture shown has the following parameters: 365 typical days per year; 1 hour per unit time; duration of a single charge and a single V2G reverse discharge is 0.3 hours and 0.8 hours respectively; the proportion of minimum charging demand met in a typical day is 0.4; the rated charging power of a single charging pile is 120 kW; the rated V2G reverse discharge power of a single charging pile is 40 kW; the energy conversion efficiency of the electric vehicle charging pile is 0.9; the maximum discharge power and maximum charging power of a single energy storage unit are 20 kW; the lower and upper limits of the energy storage state of state are 0.1 and 0.9 respectively. The installed capacity of a single energy storage unit is 60 kWh; the initial capacity of a single energy storage unit is 20 kWh; the unit time cost is 20 yuan / hour; the price of a single battery is 80,000 yuan; the cycle life of the battery when used as a vehicle power battery and as energy storage in a charging station is 2,000 cycles and 1,000 cycles, respectively; the design capacity of the battery is 60 kWh; the health of the battery when used as a vehicle power battery and as energy storage in a charging station is 0.9 and 0.7, respectively; the rated voltage of the distribution network is 10 kV, and the upper and lower limits of the distribution network bus voltage are 10.5 kV and 9.5 kV, respectively; the photovoltaic output characteristic curve and time-of-use electricity price are as follows: Figure 2 and Figure 3 As shown), a vehicle-to-grid bidirectional energy optimization interaction method for autonomous electric vehicles is implemented, such as... Figure 4 As shown, it includes the following steps:

[0060] 1. Construct a two-way energy optimization interaction model for the power-transportation coupled network.

[0061] The bidirectional energy optimization interaction model treats the power-transportation coupled network as a whole, and its optimization objective is to minimize the overall charging cost of autonomous electric vehicles. This minimized overall charging cost includes the cost C of charging stations purchasing electricity from the upper-level grid. E Battery loss cost C B User time cost C T The schematic diagram of the charging system's architecture and operation is shown below. Figure 5 As shown, autonomous electric vehicles can travel to charging stations in two ways to perform charging and V2G reverse discharge: one is driven by the electric vehicle owner, performing charging or discharging operations during the trip; the other is that the electric vehicle uses autonomous driving mode and automatically travels to the charging station to perform charging or discharging operations during the owner's idle time. Since only the first case consumes the user's time cost, only the time cost of performing charging or discharging operations during the trip is considered when calculating the user's time cost. The objective function of the model is shown in equations (1)-(4):

[0062] min C E +C B +C T (1)

[0063]

[0064] In the above formula, C E The cost of purchasing electricity from the upper-level power grid for charging stations; C B For battery wear and tear costs; C T d represents the user's time cost; d represents the number of typical days in a year; t represents any time period within a typical day. Let t be the active power output by the distribution network to the charging station at node e; C is the electricity purchase price for time period t; BV and C BE These are the daily battery loss costs consumed when the battery is used as the on-board power battery for electric vehicles and as energy storage for charging stations. For time period t, the traffic flow of electric vehicles driven by car owners heading to the charging station via path k. For time period t, the flow of electric vehicles driven by car owners using path k to the charging station to perform V2G reverse discharge; Let be the correlation coefficient between path k and road l; Let T be the travel time on road l during time period t; CH and T DI These represent the duration of a single charge and a single V2G reverse discharge, respectively; PR T Cost per unit of time.

[0065] The constraints of the model include:

[0066] (1) Vehicle charging and discharging guidance constraints

[0067] The model established in this invention considers two electric vehicle battery states: insufficient power and sufficient power. The former indicates that the electric vehicle needs to be charged and is not suitable for V2G reverse discharge, while the latter indicates that the electric vehicle does not need to be charged and can perform V2G reverse discharge. Equations (5)-(12) are the vehicle charging and discharging guidance constraints of the model. Equations (5)-(6) are the number constraints of electric vehicles with insufficient power and sufficient power at each node, respectively; Equations (7)-(8) are the number constraints of electric vehicles in the usage state and the idle state at each node, respectively; Equation (9) is the minimum charging demand constraint to be met in a typical day; Equations (10)-(11) are the number constraints of electric vehicles charging and V2G reverse discharge at the charging station, respectively; Equation (12) is the upper limit constraint of the number of charging piles in the charging station, that is, the number of vehicles charging and V2G reverse discharge in any period of time cannot exceed the number of charging piles in the charging station. Therefore, the specific settings of the vehicle charging and discharging guidance constraints are as follows:

[0068]

[0069] In the above formula, and For time period t, the vehicle flow of car owners driving electric vehicles and autonomous electric vehicles traveling to the charging station via path k are respectively: is the correlation coefficient between path k and the starting point or. If it is 1, then the starting point of path k is or; if it is 0, then the starting point of path k is not or. k is the path to the charging station for charging and discharging. or and cs are the starting point of the journey and the node where the charging station is located, respectively. and The traffic flow of owner-driven electric vehicles and autonomous electric vehicles that travel to the charging station via path k to perform V2G reverse discharge during time period t; and For time period t, the number of electric vehicles with insufficient or sufficient power at the starting point or point are respectively: and Let t be the number of electric vehicles in use and idle states at the starting point or point in time period t. and λ represents the number of charging piles at node cs used for charging and V2G reverse discharge, respectively, during time period t; R This represents the proportion of the minimum charging demand that must be met within a typical day. Let T be the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, then the node where the target charging station of path k is located is cs; if it is 0, then the node where the target charging station of path k is located is not cs.CH and T DI These represent the average duration of a single charge and the average duration of V2G reverse discharge, respectively; T U Unit of time; This represents the number of charging piles at the charging station located at node cs.

[0070] (2) Charging station operation constraints

[0071] In this constraint, equations (13)-(18) are the charging station operation constraints of the optimization model. Equation (13) is the power balance constraint of the charging station; equation (14) is the charging power constraint of the charging station; equation (15) is the V2G reverse discharge power constraint of the charging station; equation (16) is the actual output constraint of photovoltaics; equations (17) and (18) are the charging and discharging power constraints and state of charge constraints of the energy storage system in the charging station, respectively; equation (19) is the cost per unit capacity of the battery during the charge and discharge cycle; equations (20)-(21) are the battery loss cost constraints during the charge and swap cycle of the battery in each application stage; equations (22)-(23) are the charging energy and discharging energy constraints of the energy storage in the charging station. The specific settings of the charging station operation constraints are as follows:

[0072]

[0073] PR UB =PR B / 2(CY V B C κ V +CY E B C κ E (19)

[0074]

[0075]

[0076] In the above formula, For time period t, the power output from the distribution network to the charging station at node e; For time period t, the actual photovoltaic power consumed by the charging station at node e; For time period t, the output or input power of the charging station at node e, when At that time, the energy storage system is in a discharging state. At this time, the energy storage system is in a charging state; For time period t, the power of V2G reverse discharge of electric vehicles in the charging station at node e; λ C Energy conversion efficiency of electric vehicle charging stations; For time period t, the charging load of electric vehicles in the charging station at node e; and For time period t, the number of charging piles at node cs used for charging and V2G reverse discharge. P is the correlation coefficient between node cs, where the charging station is located, and node e, in the distribution network. If it is 1, then the charging station at node cs is connected to node e in the distribution network. UC P is the rated charging power of a single charging station. UD The rated V2G reverse discharge power of a single charging pile; P represents the maximum photovoltaic power output at the charging station at node e during time period t. CM and P DM These are the maximum discharge power and maximum charging power of a single energy storage unit, respectively. η represents the number of energy storage systems configured within the charging station at distribution network node e; L and η H These are the lower and upper limits of the energy state of energy storage, respectively; EC E For the installed capacity of a single energy storage unit; EC O The initial charge of a single energy storage unit; PR UB Cost per unit capacity during battery charge-discharge cycles; PR B Price per battery; CY V and CY E These refer to the cycle life of the battery when used as a vehicle power battery and as an energy storage device in a charging station, respectively; B C Design capacity for a single battery; κ V and κ E The health of the battery is measured when it is used as a vehicle power battery and when it is used as an energy storage device in a charging station. and Let t represent the charging energy and discharging energy stored in the charging station at node e during time period t. During the same time period, one of the two must be 0. For the binary variable representing the operating state of energy storage, when Energy storage and discharge, Energy storage and charging; A t,e These are auxiliary variables used in the McCormick envelope method.

[0077] (3) Constraints of the power-transportation coupled network

[0078] Equations (29)-(34) represent the distribution network operation constraints of the vehicle-network bidirectional energy optimization interaction model for the entire life cycle of autonomous electric vehicles proposed in this invention. Equations (29) and (30) are the active and reactive power balance constraints of the distribution network nodes, respectively; Equation (31) is the distribution network line capacity constraint; and Equations (32)-(34) are the distribution network node voltage constraints. The specific settings of the power-transportation coupled network constraints are as follows:

[0079]

[0080] In the above formula, and These represent the active and reactive power on line w at time t, respectively. and These represent the basic active and reactive loads connected at node e of the distribution network during time period t; For time period t, the power output from the distribution network to the charging station at node e; w represents all lines connected to node e of the distribution network; LC w The capacity of the distribution network line w; ΔU t,w The voltage drop on the distribution network line w during time period t; and These represent the resistance and reactance of the distribution network line w, respectively; U N U is the rated voltage of the distribution network busbar; t,a and U t,b U represents the bus voltages of distribution network nodes a and b during time period t, where nodes a and b are the two endpoints of distribution network line w; t,e U is the bus voltage of distribution network node e within time period t; m and U M These are the upper and lower limits of the voltage of the distribution network bus, respectively. The correlation coefficient between path k and road l; FT t,l For time period t, the total traffic flow on road l; and For time period t, the vehicle flow of car owners driving electric vehicles and autonomous electric vehicles traveling to the charging station via path k are respectively: and For time period t, the vehicle flow of owner-driven electric vehicles and autonomous electric vehicles traveling to the charging station via path k to perform V2G reverse discharge; FC l This represents the maximum traffic capacity of road l.

[0081] 2. Solve the bidirectional energy optimization interaction model of the power-transportation coupled network to obtain the vehicle-network bidirectional energy optimization interaction scheme for autonomous electric vehicles. The two charging stations are located on roads T6-T8 (12 charging piles) and T10-T11 (16 charging piles), respectively.

[0082] To verify the effectiveness of the method proposed in this invention, a one-way charging guidance strategy that only considers the driver's driving was applied as Strategy 2 to the 20-node distribution network and 13-node transportation network. The economic efficiency was compared with that of the method proposed in this invention (Strategy 1). The results are shown in Table 1:

[0083] Table 1 Economic Comparison

[0084] Strategy Annual electricity purchase cost (yuan) Annual battery wear cost (yuan) Annual time cost (yuan) Annual comprehensive charging cost (yuan) Strategy 1 7.02 x 10 6 ]] 5.53 x 10 6 ]] 2.37 x 10 6 ]] 1.49 x 10 7 ]] Strategy 2 8.89 x 10 6 ]] 5.07 x 10 6 ]] 4.14 x 10 6 ]]> 1.81 x 10 6 ]]> ;

[0085] The comparison shows that Strategy 1 results in lower annual electricity purchase costs, lower annual battery depreciation costs, and lower overall charging costs. In terms of annual electricity purchase costs for charging stations, Strategy 1 reduces costs by 21.03% compared to Strategy 2; in terms of annual time costs, Strategy 1 reduces costs by 42.75% compared to Strategy 2. In terms of overall annual charging costs, Strategy 1 reduces costs by 17.68% compared to Strategy 2. Therefore, the vehicle-to-grid bidirectional energy optimization interaction method for autonomous electric vehicles proposed in this invention satisfies the constraints of the power-transportation network while utilizing the autonomous driving function of electric vehicles to achieve flexible bidirectional interaction between vehicles and the network, effectively reducing the overall annual charging cost of the system.

[0086] Example 2:

[0087] like Figure 6 As shown, a vehicle-to-grid bidirectional energy optimization interaction system for autonomous electric vehicles is disclosed, the system comprising a model building module and a model solving module;

[0088] The model building module is used to build a two-way energy optimization interaction model of the power-transportation coupled network. The two-way energy optimization interaction model of the power-transportation coupled network aims to minimize the overall charging cost of autonomous electric vehicles, and takes into account the owner driving mode, the unmanned driving mode of electric vehicles, as well as the charging of electric vehicles and V2G reverse discharge.

[0089] The objective function of the two-way energy optimization interaction model of the power-transportation coupled network includes:

[0090] min C E +C B +C T ;

[0091]

[0092] In the above formula, C E The cost of purchasing electricity from the upper-level power grid for charging stations; C B For battery wear and tear costs; C T d represents the user's time cost; d represents the number of typical days in a year; t represents any time period within a typical day. Let t be the active power output by the distribution network to the charging station at node e; C is the electricity purchase price for time period t; BV and C BE These are the daily battery loss costs consumed when the battery is used as the on-board power battery for electric vehicles and as energy storage for charging stations. For time period t, the traffic flow of electric vehicles driven by car owners heading to the charging station via path k. For time period t, the flow of electric vehicles driven by car owners using path k to the charging station to perform V2G reverse discharge; Let be the correlation coefficient between path k and road l; Let T be the travel time on road l during time period t; CH and T DI These represent the duration of a single charge and a single V2G reverse discharge, respectively; PR T Cost per unit of time.

[0093] The bidirectional energy optimization interaction model of the power-transportation coupled network considers vehicle charging and discharging guidance constraints, charging station operation constraints considering the entire life cycle of electric vehicle batteries, and power-transportation coupled network constraints.

[0094] The vehicle charging and discharging guidance constraints include:

[0095]

[0096] In the above formula, and For time period t, the vehicle flow of car owners driving electric vehicles and autonomous electric vehicles traveling to the charging station via path k are respectively: is the correlation coefficient between path k and the starting point or. If it is 1, then the starting point of path k is or; k is the path to the charging station for charging and discharging; or and cs are the starting point of the journey and the node where the charging station is located, respectively. and The traffic flow of owner-driven electric vehicles and autonomous electric vehicles that travel to the charging station via path k to perform V2G reverse discharge during time period t; and For time period t, the number of electric vehicles with insufficient or sufficient power at the starting point or point are respectively: and Let t be the number of electric vehicles in use and idle states at the starting point or point in time period t. and λ represents the number of charging piles at node cs used for charging and V2G reverse discharge, respectively, during time period t; R This represents the proportion of the minimum charging demand that must be met within a typical day. Let T be the correlation coefficient between path k and the node cs where the charging station is located. If it is 1, then the node cs is the target charging station of path k. CH and T DI These represent the average duration of a single charge and the average duration of V2G reverse discharge, respectively; T U Unit of time; This represents the number of charging piles at the charging station located at node cs.

[0097] The operational constraints of the charging station include:

[0098]

[0099] PR UB =PR B / 2(CY V B C κ V +CY E B C κ E );

[0100]

[0101]

[0102] In the above formula, For time period t, the power output from the distribution network to the charging station at node e; For time period t, the actual photovoltaic power consumed by the charging station at node e; For time period t, the output or input power of the charging station at node e, when At that time, the energy storage system is in a discharging state. At this time, the energy storage system is in a charging state; For time period t, the power of V2G reverse discharge of electric vehicles in the charging station at node e; λ C Energy conversion efficiency of electric vehicle charging stations; For time period t, the charging load of electric vehicles in the charging station at node e; and For time period t, the number of charging piles at node cs used for charging and V2G reverse discharge. P is the correlation coefficient between node cs, where the charging station is located, and node e, in the distribution network. If it is 1, then the charging station at node cs is connected to node e in the distribution network. UC P is the rated charging power of a single charging station. UD The rated V2G reverse discharge power of a single charging pile; P represents the maximum photovoltaic power output at the charging station at node e during time period t. CM and P DM These are the maximum discharge power and maximum charging power of a single energy storage unit, respectively. η represents the number of energy storage systems configured within the charging station at distribution network node e; L and η H These are the lower and upper limits of the energy state of energy storage, respectively; EC E For the installed capacity of a single energy storage unit; EC O The initial charge of a single energy storage unit; PR UBCost per unit capacity during battery charge-discharge cycles; PR B Price per battery; CY V and CY E These refer to the cycle life of the battery when used as a vehicle power battery and as an energy storage device in a charging station, respectively; B C Design capacity for a single battery; κ V and κ E The health of the battery is measured when it is used as a vehicle power battery and when it is used as an energy storage device in a charging station. and Let t represent the charging energy and discharging energy stored in the charging station at node e during time period t. During the same time period, one of the two must be 0. For the binary variable representing the operating state of energy storage, when Energy storage and discharge, Energy storage and charging; A t,e These are auxiliary variables used in the McCormick envelope method.

[0103] The constraints of the power-transportation coupled network include:

[0104]

[0105] In the above formula, and These represent the active and reactive power on line w at time t, respectively. and These represent the basic active and reactive loads connected at node e of the distribution network during time period t; For time period t, the power output from the distribution network to the charging station at node e; w represents all lines connected to node e of the distribution network; LC w The capacity of the distribution network line w; ΔU t,w The voltage drop on the distribution network line w during time period t; and These represent the resistance and reactance of the distribution network line w, respectively; U N U is the rated voltage of the distribution network busbar; t,a and U t,b U represents the bus voltages of distribution network nodes a and b during time period t, where nodes a and b are the two endpoints of distribution network line w; t,e U is the bus voltage of distribution network node e within time period t; m and U M These are the upper and lower limits of the voltage of the distribution network bus, respectively. The correlation coefficient between path k and road l; FT t,l For time period t, the total traffic flow on road l; and For time period t, the vehicle flow of car owners driving electric vehicles and autonomous electric vehicles traveling to the charging station via path k are respectively: and For time period t, the vehicle flow of owner-driven electric vehicles and autonomous electric vehicles traveling to the charging station via path k to perform V2G reverse discharge; FC l This represents the maximum traffic capacity of road l.

[0106] The model solving module is used to solve the two-way energy optimization interaction model of the power-transportation coupled network, and obtain the vehicle-network two-way energy optimization interaction scheme for autonomous electric vehicles.

Claims

1.A method for vehicle-to-grid (V2G) bi-directional energy optimization interaction of an autonomous electric vehicle, the method comprising: S1. constructing a V2G bi-directional energy optimization interaction model of a power-traffic coupled network, the V2G bi-directional energy optimization interaction model aiming to minimize the comprehensive charging cost of the autonomous electric vehicle, and considering the driving mode of the owner of the electric vehicle, the autonomous driving mode, and the charging and V2G reverse discharging of the electric vehicle; and S2. solving the V2G bi-directional energy optimization interaction model of the power-traffic coupled network to obtain a V2G bi-directional energy optimization interaction scheme of the autonomous electric vehicle; wherein the objective function of the V2G bi-directional energy optimization interaction model of the power-traffic coupled network comprises: wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network considers a vehicle charging and discharging guidance constraint, the vehicle charging and discharging guidance constraint comprising: wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network considers a charging station operation constraint of the whole life cycle of the battery of the electric vehicle, the charging station operation constraint comprising: wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network considers a power-traffic coupled network constraint, the power-traffic coupled network constraint comprising: 2.The method of claim 1, wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network further considers the charging station operation constraint of the whole life cycle of the battery of the electric vehicle, the charging station operation constraint comprising: 3.The method of claim 2, wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network further considers the power-traffic coupled network constraint, the power-traffic coupled network constraint comprising: 4.A system for V2G bi-directional energy optimization interaction of an autonomous electric vehicle, the system comprising: a model construction module and a model solving module; wherein the model construction module is configured to construct a V2G bi-directional energy optimization interaction model of a power-traffic coupled network, the V2G bi-directional energy optimization interaction model aiming to minimize the comprehensive charging cost of the autonomous electric vehicle, and considering a vehicle charging and discharging guidance constraint; and the model solving module is configured to solve the V2G bi-directional energy optimization interaction model of the power-traffic coupled network to obtain a V2G bi-directional energy optimization interaction scheme of the autonomous electric vehicle; wherein the objective function of the V2G bi-directional energy optimization interaction model of the power-traffic coupled network comprises: wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network considers the vehicle charging and discharging guidance constraint, the vehicle charging and discharging guidance constraint comprising: wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network considers a charging station operation constraint of the whole life cycle of the battery of the electric vehicle, the charging station operation constraint comprising: wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network considers a power-traffic coupled network constraint, the power-traffic coupled network constraint comprising: 5.The system of claim 4, wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network further considers the charging station operation constraint of the whole life cycle of the battery of the electric vehicle, the charging station operation constraint comprising: ； ； ； ； In the above formula, is the cost of electricity purchased by the charging station from the upper-level power grid; is the user time cost; is the number of typical days in a year; is any time period in a typical day; is the time period , the active power output by the power distribution network to the charging station at the node ; is the electricity purchase price of the time period ; respectively are the daily battery loss costs consumed by the battery as the electric vehicle on-board power battery and the charging station energy storage; is the vehicle flow of the driver of the electric vehicle using the path to charge at the charging station in the time period ; is the vehicle flow of the driver of the electric vehicle using the path to perform V2G reverse discharge at the charging station in the time period ; is the correlation coefficient of the path and the road ; is the travel duration of the road in the time period ; respectively are the durations of single charging and single V2G reverse discharge; is the unit time cost;​​​ 6.The system of claim 5, wherein the V2G bi-directional energy optimization interaction model of the power-traffic coupled network further considers the power-traffic coupled network constraint, the power-traffic coupled network constraint comprising: ； ； ； ； ； ； ； ； In the above formula, and are the vehicle flow of the owner-driven electric vehicle and the autonomous electric vehicle using the path to charge at the charging station in time period ; is the correlation coefficient of the path and the starting point , and if it is 1, the starting point of the path is ; is the path to charge and discharge at the charging station; and are the starting point of the trip and the node where the charging station is located, respectively; and are the vehicle flow of the owner-driven electric vehicle and the autonomous electric vehicle using the path to perform V2G reverse discharge at the charging station in time period ; and are the number of electric vehicles with insufficient power and sufficient power at the starting point in time period ; and are the number of electric vehicles in use and in idle state at the starting point in time period ; and are the number of charging piles for charging and V2G reverse discharge at the charging station at the node in time period ; is the proportion of the minimum charging demand met in a typical day; is the correlation coefficient of the path and the node where the charging station is located , and if it is 1, the node where the target charging station of the path is located is ; and are the average duration of single charging and V2G reverse discharge, respectively; is the unit of time; is the number of charging piles of the charging station at the node . ​ ​ ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the above formula, is the power output by the distribution network to the charging station at node during the time period; is the actual PV power consumed in the charging station at node during the time period; is the output or input power in the charging station at node during the time period, when the energy storage system is in discharging state, and when the energy storage system is in charging state; is the power of V2G reverse discharge of electric vehicles in the charging station at node during the time period; is the energy conversion efficiency of electric vehicle charging piles; is the charging load of electric vehicles in the charging station at node and are respectively the number of charging piles for charging and V2G reverse discharge in the charging station at node during the time period; during the time period; is the correlation coefficient of the node where the charging station is located and the distribution network node , if it is 1, the charging station at node is connected to the distribution network node ; is the rated charging power of a single charging pile; is the maximum power of PV in the charging station at node and are respectively the maximum discharging power and the maximum charging power of a single energy storage; is the number of energy storage systems in the charging station at node and are respectively the lower limit and the upper limit of the energy state of the energy storage; is the installed capacity of a single energy storage; is the cost per unit capacity of battery charging and discharging cycle; is the cycle life of the battery as a vehicle-mounted power battery and a charging station energy storage, respectively; Design capacity of a single battery; and Health of the battery when used as a vehicle power battery and as a storage energy in a charging station, respectively; and respectively Period, the charging energy and discharging energy of the storage energy in the charging station at node in the same period, one of the two must be 0; Binary variable of the storage operation state, when the storage discharges, the storage charges; Auxiliary variable used in the McCormick envelope method. ​ ​ ； ； ； ； ； ； ； ； In the above equations, and are the active and reactive power on the line at time ; and are the active and reactive load connected to the distribution grid node at time ; is the power output by the charging station connected to the distribution grid node at time ; is the set of lines connected to the distribution grid node ; is the capacity of the distribution grid line ; is the voltage drop on the distribution grid line at time ; and are the resistance and reactance of the distribution grid line ; is the nominal voltage of the distribution grid bus; and are the bus voltage of the distribution grid node at time and ; and are the two endpoints of the distribution grid line ; is the bus voltage of the distribution grid node at time ; and are the upper and lower limits of the distribution grid bus voltage; is the association between the path and the road ; is the total traffic flow on the road at time ; and are the vehicle flows of the owner-driven and autonomous electric vehicles, respectively, that use the path to charge at the charging station at time ; and are the vehicle flows of the owner-driven and autonomous electric vehicles, respectively, that use the path to perform V2G reverse discharge at the charging station at time ; is the upper limit of the traffic capacity of the road . ​ ​ ​ ​ ​ ； ； ； ； In the above formula, is the cost of the charging station to purchase electricity from the upper-level power grid; is the battery loss cost; is the user time cost; is the number of typical days in a year; is any time period in a typical day; is the time period , the active power output by the distribution network to the charging station at the node ; is the time period , the electricity purchase price; and are the daily battery loss costs consumed by the battery as the electric vehicle on-board power battery and the charging station energy storage, respectively; is the time period , the vehicle flow of the driver driving the electric vehicle to the charging station for charging using the path ; is the time period , the vehicle flow of the driver driving the electric vehicle to the charging station for V2G reverse discharge using the path ; is the correlation coefficient of the path and the road ; is the time period , the passing time of the road ; and are the time lengths of single charging and single V2G reverse discharge, respectively; is the unit time cost; ​ ； ； ； ； ； ； ； ； In the above formula, and are the time periods , respectively, the vehicle flow of the electric vehicles driven by the car owners and the autonomous electric vehicles using the path to charge at the charging station; is the correlation coefficient of the path and the starting point , and if it is 1, the starting point of the path is ; is the path to charge and discharge at the charging station; and are the starting point of the trip and the node where the charging station is located, respectively; and are the time periods , respectively, the vehicle flow of the electric vehicles driven by the car owners and the autonomous electric vehicles using the path to perform V2G reverse discharge at the charging station; and are the number of electric vehicles with insufficient power and sufficient power at the starting point at the time period ; and are the number of electric vehicles in the use state and the idle state at the starting point at the time period ; and are the number of charging piles for charging and V2G reverse discharge at the charging station at the node at the time period ; is the proportion of the minimum charging demand met in a typical day; is the correlation coefficient of the path and the node where the charging station is located , and if it is 1, the node where the target charging station of the path is located is ; and are the average duration of single charging and V2G reverse discharge, respectively; is the unit time; is the number of charging piles of the charging station at the node . ​ ​ ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the above formula, is the power output by the power distribution network to the charging station at node during the time period; is the actual PV power consumed in the charging station at node during the time period; is the output or input power in the charging station at node during the time period, when the energy storage system is in discharging state, and when the energy storage system is in charging state; is the power of V2G reverse discharge of electric vehicles in the charging station at node during the time period; is the energy conversion efficiency of the electric vehicle charging pile; is the charging load of electric vehicles in the charging station at node and are respectively the number of charging piles for charging and V2G reverse discharge of the charging station at node during the time period; is the correlation coefficient of the node where the charging station is located and the power distribution network node , if it is 1, the charging station at node is connected to the power distribution network node ; is the rated charging power of a single charging pile; is the maximum power of PV in the charging station at node during the time period; and are respectively the maximum discharging power and the maximum charging power of a single energy storage; is the number of energy storage systems in the charging station at the power distribution network node and are respectively the lower limit and the upper limit of the energy state of the energy storage; is the installed capacity of a single energy storage; is the cost per unit capacity of battery charging and discharging cycle; is the cycle life of the battery as a vehicle-mounted power battery and a charging station energy storage. Design capacity of individual battery; and Health of the battery when used as a vehicle power battery and as a storage energy in charging station, respectively; and Respectively Period, the charging energy and discharging energy of the storage energy in the charging station at the node In the same period, one of the two must be 0; Binary variable of the storage operation state, when The storage discharges, The storage charges; Auxiliary variable used in the McCormick envelope method. ​ ​ ； ； ； ； ； ； ； ； In the above equations, and are the active and reactive power on the line at time ; and are the active and reactive load connected to the distribution grid node at time ; is the power output by the distribution grid to the charging station at node at time ; is the set of all lines connected to the distribution grid node ; is the capacity of the distribution grid line ; is the voltage drop on the distribution grid line at time ; are the resistance and reactance of the distribution grid line ; is the distribution grid busbar nominal voltage; and are the busbar voltage at the distribution grid node at time and ; and are the two endpoints of the distribution grid line ; is the busbar voltage at the distribution grid node at time ; and are the upper and lower limits of the distribution grid busbar voltage; is the association between path and road ; is the total traffic flow on road at time ; and are the vehicle flows of owner-driven and autonomous electric vehicles, respectively, that use path to reach the charging station at time ; and are the vehicle flows of owner-driven and autonomous electric vehicles, respectively, that use path to perform V2G reverse discharge at the charging station at time ; is the upper limit of the traffic capacity of road .

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